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Atomic Cluster Expansion: A framework for fast and accurate ML force fields
Lec 43 Machine learned interatomic potentials hands on
Convenient and efficient development of Machine Learning Interatomic Potentials
Daniel Schwalbe Koda: Machine learning for interatomic potentials
Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA
Beyond Interatomic Potentials - Further Acceleration of Atomic-Scale SImulations
Machine Learning Interatomic Potential Development with MAML
Gabor Csányi - Machine learning potentials: from polynomials to message passing networks
Ralf Drautz - From electrons to the simulation of materials - IPAM at UCLA
Lec 40 Introduction to machine learned potentials
Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA
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Last Updated: August 16, 2026
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